| --- |
| license: apache-2.0 |
| task_categories: |
| - text-generation |
| - sentence-similarity |
| language: |
| - en |
| tags: |
| - code |
| pretty_name: NPset-2-Python-Edu |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
|  |
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| # NPset-2 (Python-Edu) |
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| A normalized semi-synthetic Python dataset for training small language models on code logic without the overhead of raw code syntax. |
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| ## Why |
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| Small language models trained on natural language corpora develop latent representations of logical constructs -- iteration, conditionals, data flow, function composition -- yet struggle to apply this reasoning to source code, where syntactic overhead (delimiters, indentation conventions, language-specific idioms) occupies a disproportionate share of the token budget, requires a vocabulary of code-specific tokens, and introduces a surface-form distribution shift relative to the model's prior knowledge. NPset-2 addresses this by normalizing Python source through an AST-based converter that strips syntactic noise while preserving the full logical structure of each program, producing a pseudocode representation composed entirely of natural language tokens that aligns more directly with the semantic representations already present in small models, allowing them to reason about what code *does* rather than expending capacity learning what it *looks like*. |
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| ## The v2 Specification |
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| NPset-2 introduces significant improvements over v1, trading some relative token compression for far lower semantic overhead. |
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| 1. **Explicit Block Scoping**: All indented blocks (if, for, while, try, with) now use numbered/named anchors: `begin if 1` ... `end if 1`. This provides unambiguous attention anchors for small models. |
| 2. **Natural Language Phrasing**: |
| * **Functions**: `function find_max with input numbers` |
| * **Calls**: `call fibonacci with n - 1` |
| * **Loops**: `exit loop` and `next loop` instead of `break` and `continue`. |
| 3. **Slicing**: Replaced symbol-heavy `[0:10]` with `starting from index 0 to 10`. |
| 4. **Semantic Normalization**: |
| * `isinstance(x, int)` -> `type of x is int` |
| * `lambda x: x+1` -> `function taking x returning x + 1` |
| * `async for` -> `async for` (removing forced underscores). |
| 5. **Strict English Filtering**: Documents with >0.5% Chinese characters are dropped, and all remaining text is scrubbed of non-ASCII characters to maintain a clean, English-only training distribution. |
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| ## Performance (Context Capacity) |
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| When tested against standard tokenizers, TinyDSL v2 significantly expands the effective context window for logic-heavy training with natural language tokenizers: |
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| | Tokenizer | Reduction (Tokens) | Context Capacity (2048 window) | |
| | :--- | :--- | :--- | |
| | **GPTX (Custom 32k)** | **13.7%** | **7.1 -> 8.3 examples (+15.9%)** | |
| | **GPT-2** | **16.6%** | **7.4 -> 8.9 examples (+19.9%)** | |
| | Qwen 2.5 | 8.1% | 10.1 -> 9.3 examples (-7.5%) | |
| | Llama 3 | 2.2% | 8.3 -> 8.1 examples (-2.2%) | |
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| *Note: While raw character counts increase by ~17%, the "Token Tax" for logical constructs is drastically reduced for models not pre-specialized for code syntax.* |
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| ## Format |
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| Parquet format with the following schema: |
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| | Field | Type | Description | |
| |---|---|---| |
| | `code` | string | Normalized TinyDSL v2 pseudocode | |
| | `original_code` | string | Original Python source | |
| | `original_language` | string | Always `python` | |
| | `score` | float | Quality/Difficulty score (if available from source) | |
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| ## Sources |
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| - `HuggingFaceTB/stack-edu (python)` |
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